Text Mining

Aim

After completion of the course the student should on an advanced level be able to: - use basic methods for information extraction and retrieval of textual data, - apply text processing techniques to prepare documents for statistical modelling, - apply relevant statistical models for analyzing textual data and correctly interpret the results, - use statistical models for prediction of textual information, - evaluate the performance of statistical models for textual data.

Content

The course presents how textual data can be retrieved, linguistically pre-processed and subsequently analyzed quantitatively using formal statistical methods and models. The course brings together expertise from the areas of database methodology, computational linguistics and statistics. The following topics are covered: Introduction and overview of quantitative text analysis and its applications; Information extraction; Web crawling; Information retrieval; Tf-idf; Vector space models; Text preprocessing; Bag of words; N-grams; Sparsity and smoothing for text; Document classification; Sentiment analysis; Model evaluation; Topic models.